Industrial Use Cases of 5G
Martti Mäntylä
Aalto University
Martti Mäntylä
• Professor of Information
Technology (Enterprise
Systems), TKK & Aalto
University 1987-
• Chief Strategy Officer, EIT ICT
Labs 2009-2013
• Director, Helsinki Institute for
Information Technology 1999-
2008
• Since 2014, catalysing Aalto’s
activities in digitalisation of
industry
The Next Industrial Revolution
Industrie 4.0
From 5-layer architecture to autonomous
cyber-physical systems
Smart and connected products
(Porter and Heppelman 2015)
The Potential Role of 5G
5G as an Industrial Internet platform?
• Today, 4G/LTE architecture
manages 3 billion mobile devices
in a multiple actor environment,
including sharing of business data
across operators
• Can 5G provide a management
and operational architecture for
20+ billion smart devices,
providing the right mix of
characteristics needed by next
generation industrial use cases?
5G: Support for heterogeneous services
5G: Machine type communications
MTC device domain
4G towards 5G
MTC application domain
MTC servers
HSS
MME
SCS S-GW
MTC-IWF
P-GW
eNodeB
MTCG
MTC
device
MTC
device
MTC
device
Network domain
MTC
device
MTC
device
MTC
device
LTE link
D2D link
Non-LTE lin k
Cloud- and network function
virtualization -based core
network
• Service and
organization dependent
virtualized EPC
• Edge computing
• Capillary networks
• Network controlled
direct device-to-device
From operational benefits to new
business value
Cluster
Key
Benefit
Key
Asset
Typical
Applications
Industrial
Internet for
operational
benefits
Operational
efficiency of
in
-house
operations
Fleets
of installed products
equipped with sensors and
connected to company
network (“Industrial Intranet”)
Service
and
maintenance operations
Industrial
Internet for
new
customer
value
New digital
services and
service
systems
Fleets
of installed products
and product systems, data
from related systems, data
from open Internet
Predictive maintenance,
remote operations,
optimisation of
processes, systems
optimisation, logistics
Industrial
Internet for
digital
business
New
business
Minimal
physical assets,
focus on data gathered via a
digital platform
Innovative applications
based on data and new
business
models
Case ABB
• Maintenance and diagnosis
– failure prediction (converter,
motor, load, ...)
• Start-up of a drive system
– numerous motor and load
parameters needed for control
– some parameters are difficult
to identify automatically using
conventional methods only
• Resource-efficient control
– self-optimization
– fusion of information from
various sources
Digital twin
Case Konecranes
Cranes/
trucks
embedded
with
sensors
Diagnostic
units/
sensors
Konecranes
remote data
center
Crane portal
Data capture
Data integration
and
management
Data usage
Equipment in
business
processes
World-class
equipment in
operation
Sensors capture
various data
Analytics, predictive
algorithms,
automation and deep
domain expertise
Improved safety,
productivity,
efficiencies, and
maintenance
Data Proc. Logic &
NW Data/
Control Slice IEV_A
Data Proc. Logic &
NW Data/
Control Slice IEV_B
Remote monitoring / fleet management
Manufacturer #1
Equipment
Vendor #A
Equipment
Vendor #B
Manufacturer #3
Manufacturer #2
In each factory different
slices for accessing data
and enabling remote
monitoring/control of
industrial equipment
provided by different
vendors
Edge Cloud
capabilities
Edge Cloud
capabilities
Edge Cloud
capabilities
Picture credit: Ivan Ferris & Tarik Taleb
Data Proc. Logic &
NW Data/
Control Slice IEV_A
Data Proc. Logic &
NW Data/
Control Slice IEV_B
Data Proc. Logic &
NW Data/
Control Slice IEV_A
Data Proc. Logic &
NW Data/
Control Slice IEV_B
Fleet management
• Remote management of equipment by its provider
– Diagnostics / predictive maintenance: Collect diagnostic data for
fault prediction and assessment, guide maintenance operations
– Life-cycle engineering: Collect diagnostic data to study how the
operations can be improved, design and deploy updates
• Stakeholders:
– Equipment providers: access to relevant data from installed base
– Customers: need to grant access to relevant data
• 5G issues
– How to provide access to all installed equipment on the field while
respecting the confidentiality requirements of the customers?
Case Forest Industry
Factory control
• Enable control of factory equipment for industrial process
optimization
– Local: E.g., private 5G network inside factory site
– Remote: E.g., network slice for data transmission between different
production sites and other parties
• 5G issues
– Spectrum management
– Latency (especially to enable remote control-by-cloud scenarios)
• On-demand provisioning of control features at the edge of the network
– Dynamic network and service chaining
– Robustness and availability
– Cyber security
– Lifecycle management
• Esp. scenarios where equipment from many vendors needs to be
managed and controlled in a single system
Work in progress @ Aalto
5G@II project
• Create a management system embedded in the 5G
architecture that will support
– secure management of the smart devices
– scalable and secure data collection and storage on the basis of
5G network slicing
– policy-based digital contracting, digital service creation and
management
– trustworthy data sharing using models rather than data itself.
• Pilot the system by combining the AIIC platform
http://aiic.aalto.fi/en/ and TAKE-5 experimental 5G
network (http://take-5g.org/) and running concrete
experiments based on industrially relevant use cases.
AIIC experimental platforms
• ABB: IoT instrumentation
for a research apparatus
for studying magnetic
bearings
• Konecranes: Smart crane
with extensive PLM
models and IoT interfaces
enabling a digital twin
• ABB et al.: Process
control lab covering
several IoT-enabled unit
processes for chemical
industry
• ACRE: Digital campus
Digital campus: bIoTope project
Use cases vs. AIIC platforms
Smart crane
Process plant
Factory
control
M2M
scenarios with strict
latency
requirements
Operator interfaces
Remote control
scenarios with strict
latency
requirements
-level
-
Factory
monitoring
/fleet
management
Managed
access to
relevant
data to the
equipment provider
Partial access to relevant
data to other stakeholders
Managed access to
relevant data to the
equipment
providers
Digital twin
Data integration scenarios
including sensor data
On
-line simulation
& control scenarios
-line